Assignment: Housing in Brazil 🇧🇷

import wqet_grader

wqet_grader.init("Project 1 Assessment")

In this assignment, you'll work with a dataset of homes for sale in Brazil. Your goal is to determine if there are regional differences in the real estate market. Also, you will look at southern Brazil to see if there is a relationship between home size and price, similar to what you saw with housing in some states in Mexico.

Note: There are are 19 graded tasks in this assignment, but you only need to complete 18. Once you've successfully completed 18 tasks, you'll be automatically enrolled in the next project, and this assignment will be closed. This means that you might not be allowed to complete the last task. So if you get an error saying that you've already complete the course, that's good news! Move to project 2.

Before you start: Import the libraries you'll use in this notebook: Matplotlib, pandas, and plotly. Be sure to import them under the aliases we've used in this project.

# Import Matplotlib, pandas, and plotly

import matplotlib.pyplot as plt
import pandas as pd
import plotly.express as px

Prepare Data

In this assignment, you'll work with real estate data from Brazil. In the data directory for this project there are two CSV that you need to import and clean.

Import

Task 1.5.1: Import the CSV file data/brasil-real-estate-1.csv into the DataFrame df1.

df1 = pd.read_csv("data/brasil-real-estate-1.csv")
df1.head()

	property_type	place_with_parent_names	region	lat-lon	area_m2	price_usd
0	apartment	|Brasil|Alagoas|Maceió|	Northeast	-9.6443051,-35.7088142	110.0	$187,230.85
1	apartment	|Brasil|Alagoas|Maceió|	Northeast	-9.6430934,-35.70484	65.0	$81,133.37
2	house	|Brasil|Alagoas|Maceió|	Northeast	-9.6227033,-35.7297953	211.0	$154,465.45
3	apartment	|Brasil|Alagoas|Maceió|	Northeast	-9.622837,-35.719556	99.0	$146,013.20
4	apartment	|Brasil|Alagoas|Maceió|	Northeast	-9.654955,-35.700227	55.0	$101,416.71

wqet_grader.grade("Project 1 Assessment", "Task 1.5.1", df1)

🥳

Score: 1

Before you move to the next task, take a moment to inspect df1 using the info and head methods. What issues do you see in the data? What cleaning will you need to do before you can conduct your analysis?

df1.shape
df1.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 12834 entries, 0 to 12833
Data columns (total 6 columns):
 #   Column                   Non-Null Count  Dtype  
---  ------                   --------------  -----  
 0   property_type            12834 non-null  object 
 1   place_with_parent_names  12834 non-null  object 
 2   region                   12834 non-null  object 
 3   lat-lon                  11551 non-null  object 
 4   area_m2                  12834 non-null  float64
 5   price_usd                12834 non-null  object 
dtypes: float64(1), object(5)
memory usage: 601.7+ KB

Task 1.5.2: Drop all rows with NaN values from the DataFrame df1.

df1.dropna(inplace = True)
df1.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 11551 entries, 0 to 12833
Data columns (total 6 columns):
 #   Column                   Non-Null Count  Dtype  
---  ------                   --------------  -----  
 0   property_type            11551 non-null  object 
 1   place_with_parent_names  11551 non-null  object 
 2   region                   11551 non-null  object 
 3   lat-lon                  11551 non-null  object 
 4   area_m2                  11551 non-null  float64
 5   price_usd                11551 non-null  object 
dtypes: float64(1), object(5)
memory usage: 631.7+ KB

wqet_grader.grade("Project 1 Assessment", "Task 1.5.2", df1)

Python master 😁

Score: 1

Task 1.5.3: Use the "lat-lon" column to create two separate columns in df1: "lat" and "lon". Make sure that the data type for these new columns is float.

df1[["lat","lon"]] = df1["lat-lon"].str.split(",",expand=True).astype(float)
    
df1.info()
df1.head(10)

<class 'pandas.core.frame.DataFrame'>
Int64Index: 11551 entries, 0 to 12833
Data columns (total 8 columns):
 #   Column                   Non-Null Count  Dtype  
---  ------                   --------------  -----  
 0   property_type            11551 non-null  object 
 1   place_with_parent_names  11551 non-null  object 
 2   region                   11551 non-null  object 
 3   lat-lon                  11551 non-null  object 
 4   area_m2                  11551 non-null  float64
 5   price_usd                11551 non-null  object 
 6   lat                      11551 non-null  float64
 7   lon                      11551 non-null  float64
dtypes: float64(3), object(5)
memory usage: 812.2+ KB

	property_type	place_with_parent_names	region	lat-lon	area_m2	price_usd	lat	lon
0	apartment	|Brasil|Alagoas|Maceió|	Northeast	-9.6443051,-35.7088142	110.0	$187,230.85	-9.644305	-35.708814
1	apartment	|Brasil|Alagoas|Maceió|	Northeast	-9.6430934,-35.70484	65.0	$81,133.37	-9.643093	-35.704840
2	house	|Brasil|Alagoas|Maceió|	Northeast	-9.6227033,-35.7297953	211.0	$154,465.45	-9.622703	-35.729795
3	apartment	|Brasil|Alagoas|Maceió|	Northeast	-9.622837,-35.719556	99.0	$146,013.20	-9.622837	-35.719556
4	apartment	|Brasil|Alagoas|Maceió|	Northeast	-9.654955,-35.700227	55.0	$101,416.71	-9.654955	-35.700227
5	apartment	|Brasil|Alagoas|Maceió|	Northeast	-9.614414,-35.735621	56.0	$75,727.07	-9.614414	-35.735621
6	apartment	|Brasil|Alagoas|Maceió|	Northeast	-9.584755,-35.662909	68.0	$110,916.18	-9.584755	-35.662909
7	apartment	|Brasil|Alagoas|Maceió|	Northeast	-9.658285,-35.703827	187.0	$249,641.14	-9.658285	-35.703827
9	apartment	|Brasil|Alagoas|Maceió|	Northeast	-9.66082,-35.702976	90.0	$115,459.02	-9.660820	-35.702976
10	apartment	|Brasil|Alagoas|Maceió|	Northeast	-9.6637998,-35.7115455	137.0	$361,979.65	-9.663800	-35.711545

wqet_grader.grade("Project 1 Assessment", "Task 1.5.3", df1)

Yes! Great problem solving.

Score: 1

Task 1.5.4: Use the "place_with_parent_names" column to create a "state" column for df1. (Note that the state name always appears after "|Brasil|" in each string.)

df1["state"] = df1["place_with_parent_names"].str.split("|",expand=True)[2]
df1.head()

	property_type	place_with_parent_names	region	lat-lon	area_m2	price_usd	lat	lon	state
0	apartment	|Brasil|Alagoas|Maceió|	Northeast	-9.6443051,-35.7088142	110.0	$187,230.85	-9.644305	-35.708814	Alagoas
1	apartment	|Brasil|Alagoas|Maceió|	Northeast	-9.6430934,-35.70484	65.0	$81,133.37	-9.643093	-35.704840	Alagoas
2	house	|Brasil|Alagoas|Maceió|	Northeast	-9.6227033,-35.7297953	211.0	$154,465.45	-9.622703	-35.729795	Alagoas
3	apartment	|Brasil|Alagoas|Maceió|	Northeast	-9.622837,-35.719556	99.0	$146,013.20	-9.622837	-35.719556	Alagoas
4	apartment	|Brasil|Alagoas|Maceió|	Northeast	-9.654955,-35.700227	55.0	$101,416.71	-9.654955	-35.700227	Alagoas

wqet_grader.grade("Project 1 Assessment", "Task 1.5.4", df1)

Boom! You got it.

Score: 1

Task 1.5.5: Transform the "price_usd" column of df1 so that all values are floating-point numbers instead of strings.

df1["price_usd"] = (
    
    df1["price_usd"]
    .str.replace("$","", regex = False)
    .str.replace(",","")
    .astype(float)

)
df1.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 11551 entries, 0 to 12833
Data columns (total 9 columns):
 #   Column                   Non-Null Count  Dtype  
---  ------                   --------------  -----  
 0   property_type            11551 non-null  object 
 1   place_with_parent_names  11551 non-null  object 
 2   region                   11551 non-null  object 
 3   lat-lon                  11551 non-null  object 
 4   area_m2                  11551 non-null  float64
 5   price_usd                11551 non-null  float64
 6   lat                      11551 non-null  float64
 7   lon                      11551 non-null  float64
 8   state                    11551 non-null  object 
dtypes: float64(4), object(5)
memory usage: 902.4+ KB

wqet_grader.grade("Project 1 Assessment", "Task 1.5.5", df1)

Yes! Keep on rockin'. 🎸That's right.

Score: 1

Task 1.5.6: Drop the "lat-lon" and "place_with_parent_names" columns from df1.

df1.drop(columns=["lat-lon", "place_with_parent_names"], inplace=True)
df1.head()

	property_type	region	area_m2	price_usd	lat	lon	state
0	apartment	Northeast	110.0	187230.85	-9.644305	-35.708814	Alagoas
1	apartment	Northeast	65.0	81133.37	-9.643093	-35.704840	Alagoas
2	house	Northeast	211.0	154465.45	-9.622703	-35.729795	Alagoas
3	apartment	Northeast	99.0	146013.20	-9.622837	-35.719556	Alagoas
4	apartment	Northeast	55.0	101416.71	-9.654955	-35.700227	Alagoas

wqet_grader.grade("Project 1 Assessment", "Task 1.5.6", df1)

Correct.

Score: 1

Task 1.5.7: Import the CSV file brasil-real-estate-2.csv into the DataFrame df2.

df2 = pd.read_csv("data/brasil-real-estate-2.csv")

wqet_grader.grade("Project 1 Assessment", "Task 1.5.7", df2)

Excellent work.

Score: 1

Before you jump to the next task, take a look at df2 using the info and head methods. What issues do you see in the data? How is it similar or different from df1?

df2.shape
df2.info()
df2.head()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 12833 entries, 0 to 12832
Data columns (total 7 columns):
 #   Column         Non-Null Count  Dtype  
---  ------         --------------  -----  
 0   property_type  12833 non-null  object 
 1   state          12833 non-null  object 
 2   region         12833 non-null  object 
 3   lat            12833 non-null  float64
 4   lon            12833 non-null  float64
 5   area_m2        11293 non-null  float64
 6   price_brl      12833 non-null  float64
dtypes: float64(4), object(3)
memory usage: 701.9+ KB

	property_type	state	region	lat	lon	area_m2	price_brl
0	apartment	Pernambuco	Northeast	-8.134204	-34.906326	72.0	414222.98
1	apartment	Pernambuco	Northeast	-8.126664	-34.903924	136.0	848408.53
2	apartment	Pernambuco	Northeast	-8.125550	-34.907601	75.0	299438.28
3	apartment	Pernambuco	Northeast	-8.120249	-34.895920	187.0	848408.53
4	apartment	Pernambuco	Northeast	-8.142666	-34.906906	80.0	464129.36

Task 1.5.8: Use the "price_brl" column to create a new column named "price_usd". (Keep in mind that, when this data was collected in 2015 and 2016, a US dollar cost 3.19 Brazilian reals.)

df2["price_usd"] = (df2["price_brl"]/3.19).round(2)
df2.head()

	property_type	state	region	lat	lon	area_m2	price_brl	price_usd
0	apartment	Pernambuco	Northeast	-8.134204	-34.906326	72.0	414222.98	129850.46
1	apartment	Pernambuco	Northeast	-8.126664	-34.903924	136.0	848408.53	265958.79
2	apartment	Pernambuco	Northeast	-8.125550	-34.907601	75.0	299438.28	93867.80
3	apartment	Pernambuco	Northeast	-8.120249	-34.895920	187.0	848408.53	265958.79
4	apartment	Pernambuco	Northeast	-8.142666	-34.906906	80.0	464129.36	145495.10

wqet_grader.grade("Project 1 Assessment", "Task 1.5.8", df2)

You got it. Dance party time! 🕺💃🕺💃

Score: 1

Task 1.5.9: Drop the "price_brl" column from df2, as well as any rows that have NaN values.

df2.drop(columns=["price_brl"], inplace=True)
df2.dropna(inplace=True)
df2.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 11293 entries, 0 to 12832
Data columns (total 7 columns):
 #   Column         Non-Null Count  Dtype  
---  ------         --------------  -----  
 0   property_type  11293 non-null  object 
 1   state          11293 non-null  object 
 2   region         11293 non-null  object 
 3   lat            11293 non-null  float64
 4   lon            11293 non-null  float64
 5   area_m2        11293 non-null  float64
 6   price_usd      11293 non-null  float64
dtypes: float64(4), object(3)
memory usage: 705.8+ KB

wqet_grader.grade("Project 1 Assessment", "Task 1.5.9", df2)

Yes! Your hard work is paying off.

Score: 1

Task 1.5.10: Concatenate df1 and df2 to create a new DataFrame named df.

df = pd.concat([df1,df2])
print("df shape:", df.shape)
df.head()

df shape: (22844, 7)

	property_type	region	area_m2	price_usd	lat	lon	state
0	apartment	Northeast	110.0	187230.85	-9.644305	-35.708814	Alagoas
1	apartment	Northeast	65.0	81133.37	-9.643093	-35.704840	Alagoas
2	house	Northeast	211.0	154465.45	-9.622703	-35.729795	Alagoas
3	apartment	Northeast	99.0	146013.20	-9.622837	-35.719556	Alagoas
4	apartment	Northeast	55.0	101416.71	-9.654955	-35.700227	Alagoas
...	...	...	...	...	...	...	...
224	apartment	Northeast	143.0	296448.85	-12.941992	-38.403465	Bahia
225	apartment	Northeast	158.0	180989.82	-12.941992	-38.403465	Bahia
226	apartment	Northeast	75.0	77388.75	-12.999527	-38.462650	Bahia
227	apartment	Northeast	128.0	235843.06	-12.982230	-38.438980	Bahia
228	house	Northeast	156.0	193471.88	-12.852844	-38.327110	Bahia

200 rows × 7 columns

wqet_grader.grade("Project 1 Assessment", "Task 1.5.10", df)

You = coding 🥷

Score: 1

Frequent Question: I can't pass this question, and I don't know what I've done wrong. 😠 What's happening?

Tip: In this assignment, you're working with data that's similar — but not identical — the data used in the lessons. That means that you might need to make adjust the code you used in the lessons to work here. Take a second look at df1 after you complete 1.5.6, and make sure you've correctly created the state names.

Explore

It's time to start exploring your data. In this section, you'll use your new data visualization skills to learn more about the regional differences in the Brazilian real estate market.

Complete the code below to create a scatter_mapbox showing the location of the properties in df.

fig = px.scatter_mapbox(
    df,
    lat="lat",
    lon="lon",
    center={"lat": -14.2, "lon": -51.9},  # Map will be centered on Brazil
    width=600,
    height=600,
    hover_data=["price_usd"],  # Display price when hovering mouse over house
)

fig.update_layout(mapbox_style="open-street-map")

fig.show()

Task 1.5.11: Use the describe method to create a DataFrame summary_stats with the summary statistics for the "area_m2" and "price_usd" columns.

summary_stats = df[["area_m2","price_usd"]].describe()
summary_stats

	area_m2	price_usd
count	22844.000000	22844.000000
mean	115.020224	194987.315515
std	47.742932	103617.682979
min	53.000000	74892.340000
25%	76.000000	113898.770000
50%	103.000000	165697.555000
75%	142.000000	246900.882500
max	252.000000	525659.720000

wqet_grader.grade("Project 1 Assessment", "Task 1.5.11", summary_stats)

Party time! 🎉🎉🎉

Score: 1

Task 1.5.12: Create a histogram of "price_usd". Make sure that the x-axis has the label "Price [USD]", the y-axis has the label "Frequency", and the plot has the title "Distribution of Home Prices".

plt.hist(df["price_usd"])
plt.xlabel("Price [USD]")
plt.ylabel("Frequency")
plt.title("Distribution of Home Prices")

# Don't change the code below
plt.savefig("images/1-5-12.png", dpi=150)

with open("images/1-5-12.png", "rb") as file:
    wqet_grader.grade("Project 1 Assessment", "Task 1.5.12", file)

🥳

Score: 1

Task 1.5.13: Create a horizontal boxplot of "area_m2". Make sure that the x-axis has the label "Area [sq meters]" and the plot has the title "Distribution of Home Sizes".

# Don't change the code below
plt.savefig("images/1-5-13.png", dpi=150)

plt.boxplot(df["area_m2"], vert=False)
plt.xlabel("Area [sq meters]")
plt.title("Distribution of Home Sizes")

Text(0.5, 1.0, 'Distribution of Home Sizes')

with open("images/1-5-13.png", "rb") as file:
    wqet_grader.grade("Project 1 Assessment", "Task 1.5.13", file)

Party time! 🎉🎉🎉

Score: 1

Task 1.5.14: Use the groupby method to create a Series named mean_price_by_region that shows the mean home price in each region in Brazil, sorted from smallest to largest.

mean_price_by_region = df.groupby("region")["price_usd"].mean().sort_values(ascending =True)
mean_price_by_region

region
Central-West    178596.283663
North           181308.958188
Northeast       185422.985482
South           189012.345360
Southeast       208996.762761
Name: price_usd, dtype: float64

wqet_grader.grade("Project 1 Assessment", "Task 1.5.14", mean_price_by_region)

Very impressive.

Score: 1

Task 1.5.15: Use mean_price_by_region to create a bar chart. Make sure you label the x-axis as "Region" and the y-axis as "Mean Price [USD]", and give the chart the title "Mean Home Price by Region".

mean_price_by_region.plot(
    kind="bar",
    xlabel= "Region",
    ylabel = "Mean Price [USD]",
    title = "Mean Home Price by Region"
);

# Don't change the code below
plt.savefig("images/1-5-15.png", dpi=150)

with open("images/1-5-15.png", "rb") as file:
    wqet_grader.grade("Project 1 Assessment", "Task 1.5.15", file)

Python master 😁

Score: 1

Keep it up! You're halfway through your data exploration. Take one last break and get ready for the final push. 🚀

You're now going to shift your focus to the southern region of Brazil, and look at the relationship between home size and price.

Task 1.5.16: Create a DataFrame df_south that contains all the homes from df that are in the "South" region.

df_south = df.loc[(df["region"]=="South")]
df_south.head()

	property_type	region	area_m2	price_usd	lat	lon	state
9304	apartment	South	127.0	296448.85	-25.455704	-49.292918	Paraná
9305	apartment	South	104.0	219996.25	-25.455704	-49.292918	Paraná
9306	apartment	South	100.0	194210.50	-25.460236	-49.293812	Paraná
9307	apartment	South	77.0	149252.94	-25.460236	-49.293812	Paraná
9308	apartment	South	73.0	144167.75	-25.460236	-49.293812	Paraná

wqet_grader.grade("Project 1 Assessment", "Task 1.5.16", df_south)

You = coding 🥷

Score: 1

Task 1.5.17: Use the value_counts method to create a Series homes_by_state that contains the number of properties in each state in df_south.

homes_by_state = df_south["state"].value_counts()
homes_by_state

Rio Grande do Sul    2643
Santa Catarina       2634
Paraná               2544
Name: state, dtype: int64

wqet_grader.grade("Project 1 Assessment", "Task 1.5.17", homes_by_state)

Yes! Great problem solving.

Score: 1

Task 1.5.18: Create a scatter plot showing price vs. area for the state in df_south that has the largest number of properties. Be sure to label the x-axis "Area [sq meters]" and the y-axis "Price [USD]"; and use the title "<name of state>: Price vs. Area".

df_Rio_Grande_do_Sul = df[df["state"]=='Rio Grande do Sul']

plt.scatter(x=df_Rio_Grande_do_Sul["area_m2"], y=df_Rio_Grande_do_Sul["price_usd"])
plt.xlabel("Area [sq meters]")
plt.ylabel("Price [USD]")
plt.title("<name of state>: Price vs. Area")

# Don't change the code below
plt.savefig("images/1-5-18.png", dpi=150)

with open("images/1-5-18.png", "rb") as file:
    wqet_grader.grade("Project 1 Assessment", "Task 1.5.18", file)

---------------------------------------------------------------------------
Exception                                 Traceback (most recent call last)
Input In [91], in <cell line: 1>()
      1 with open("images/1-5-18.png", "rb") as file:
----> 2     wqet_grader.grade("Project 1 Assessment", "Task 1.5.18", file)

File /opt/conda/lib/python3.9/site-packages/wqet_grader/__init__.py:180, in grade(assessment_id, question_id, submission)
    175 def grade(assessment_id, question_id, submission):
    176   submission_object = {
    177     'type': 'simple',
    178     'argument': [submission]
    179   }
--> 180   return show_score(grade_submission(assessment_id, question_id, submission_object))

File /opt/conda/lib/python3.9/site-packages/wqet_grader/transport.py:143, in grade_submission(assessment_id, question_id, submission_object)
    141     raise Exception('Grader raised error: {}'.format(error['message']))
    142   else:
--> 143     raise Exception('Could not grade submission: {}'.format(error['message']))
    144 result = envelope['data']['result']
    146 # Used only in testing

Exception: Could not grade submission: Could not verify access to this assessment: Received error from WQET submission API: You have already passed this course!

Task 1.5.19: Create a dictionary south_states_corr, where the keys are the names of the three states in the "South" region of Brazil, and their associated values are the correlation coefficient between "area_m2" and "price_usd" in that state.

As an example, here's a dictionary with the states and correlation coefficients for the Southeast region. Since you're looking at a different region, the states and coefficients will be different, but the structure of the dictionary will be the same.

{'Espírito Santo': 0.6311332554173303,
 'Minas Gerais': 0.5830029036378931,
 'Rio de Janeiro': 0.4554077103515366,
 'São Paulo': 0.45882050624839366}

df_parana = df[df["state"]=='Paraná']
df_santa_catarina = df[df["state"]=='Santa Catarina']
df_Rio_Grande_do_Sul = df[df["state"]=='Rio Grande do Sul']

south_states_corr = {
    'Paraná' : df_parana["area_m2"].corr(df_parana["price_usd"]),
    'Santa Catarina' : df_santa_catarina["area_m2"].corr(df_santa_catarina["price_usd"]),
    'Rio Grande do Sul' : df_Rio_Grande_do_Sul["area_m2"].corr(df_Rio_Grande_do_Sul["price_usd"])
}

south_states_corr

{'Paraná': 0.5436659935502659,
 'Santa Catarina': 0.5068121769989853,
 'Rio Grande do Sul': 0.5773267433871903}

wqet_grader.grade("Project 1 Assessment", "Task 1.5.19", south_states_corr)

Yes! Keep on rockin'. 🎸That's right.

Score: 1

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